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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import imageio
import torch
from cosmos1.models.autoregressive.inference.world_generation_pipeline import ARBaseGenerationPipeline
from cosmos1.models.autoregressive.utils.inference import add_common_arguments, load_vision_input, validate_args
from .log import log
def parse_args():
parser = argparse.ArgumentParser(description="Video to world generation demo script")
# Add common arguments
add_common_arguments(parser)
parser.add_argument(
"--ar_model_dir",
type=str,
default="Cosmos-1.0-Autoregressive-4B",
)
parser.add_argument("--input_type", type=str, default="video", help="Type of input", choices=["image", "video"])
args = parser.parse_args()
return args
def main(args):
"""Run video-to-world generation demo.
This function handles the main video-to-world generation pipeline, including:
- Setting up the random seed for reproducibility
- Initializing the generation pipeline with the provided configuration
- Processing single or multiple images/videos from input
- Generating videos from images/videos
- Saving the generated videos to disk
Args:
cfg (argparse.Namespace): Configuration namespace containing:
- Model configuration (checkpoint paths, model settings)
- Generation parameters (temperature, top_p)
- Input/output settings (images/videos, save paths)
- Performance options (model offloading settings)
The function will save:
- Generated MP4 video files
If guardrails block the generation, a critical log message is displayed
and the function continues to the next prompt if available.
"""
inference_type = "base" # When the inference_type is "base", AR model does not take text as input, the world generation is purely based on the input video
sampling_config = validate_args(args, inference_type)
# Initialize base generation model pipeline
pipeline = ARBaseGenerationPipeline(
inference_type=inference_type,
checkpoint_dir=args.checkpoint_dir,
checkpoint_name=args.ar_model_dir,
disable_diffusion_decoder=args.disable_diffusion_decoder,
offload_guardrail_models=args.offload_guardrail_models,
offload_diffusion_decoder=args.offload_diffusion_decoder,
offload_network=args.offload_ar_model,
offload_tokenizer=args.offload_tokenizer,
)
# Load input image(s) or video(s)
input_videos = load_vision_input(
input_type=args.input_type,
batch_input_path=args.batch_input_path,
input_image_or_video_path=args.input_image_or_video_path,
data_resolution=args.data_resolution,
num_input_frames=args.num_input_frames,
)
for idx, input_filename in enumerate(input_videos):
inp_vid = input_videos[input_filename]
# Generate video
log.info(f"Run with image or video path: {input_filename}")
out_vid = pipeline.generate(
inp_vid=inp_vid,
num_input_frames=args.num_input_frames,
seed=args.seed,
sampling_config=sampling_config,
)
if out_vid is None:
log.critical("Guardrail blocked base generation.")
continue
# Save video
if args.input_image_or_video_path:
out_vid_path = os.path.join(args.video_save_folder, f"{args.video_save_name}.mp4")
else:
out_vid_path = os.path.join(args.video_save_folder, f"{idx}.mp4")
imageio.mimsave(out_vid_path, out_vid, fps=25)
log.info(f"Saved video to {out_vid_path}")
if __name__ == "__main__":
torch._C._jit_set_texpr_fuser_enabled(False)
args = parse_args()
main(args)
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